Application of Self-Tuning Pid Control Based on Diagonal Recurrent Neural Network in Crystallization Process

In crystallization process there are strong coupling between the temperature control and the level control in the tank, and the parameter is time-varying, so it is difficult to apply general PID control. The self-adjusting PID control method based on diagonal recurrent neural network (DRNN) is introduced in this paper. According to the influence of object's parameter to system output performance, the DRNN can auto-adjust its weights to vary k_P, k_I and k_D . The Simulation results show that the presented control system has quick response speed and strong adaptive capability